How an Open Analytics Ecosystem Became a Lifesaver

Description

Given the diverse talent and skill sets of today’s Data Scientist, it is time for an analytic platform where you should not have to choose a single approach. To be viable in the open ecosystem of today’s economy and analytics, methods have to be open and integrated. You should not have to choose between analytics languages like Python, R, or SAS. Find out how you can literally have it all.

Abstract:

Given the diverse talent and skill sets of today’s Data Scientist, it is time for a platform where you should not have to choose a single analytical approach. To be viable in the open ecosystem of today’s economy and analytics, methods have to be open and integrated. We explain where SAS fits into the open ecosystem, why you no longer have to choose between analytics languages like Python, R, or SAS, and how a single, unified open analytics architecture empowered by Docker containers and thin clients, like Jupyter Notebooks, may allow you to literally have it all.

We detail a case study involving one of the world’s largest health agencies. Dependent on donations and fundraising efforts, this agency suffered under the weight of data silos and data quality issues. Answering relevant business questions was both difficult and time-consuming. With help from python, Jupyter Notebooks, SAS and an open analytics ecosystem, the company now makes data-driven decisions based on previously undetectable patterns in data; as a result, employees spend less time generating reports, allowing more time to work directly with patients, researchers, donors, and participants.

We also explore how embracing Docker container technology and Jupyter Notebooks can help you provide a flexible infrastructure with all the tools and methods you need in one place. Join in to learn how strategic placement of containers helps push work inside the Hadoop cluster, minimizing data movement, minimizing time to insight, and maximizing the value of robust analytics.